tensorflow_datasets.zip

TensorFlow Datasets 是一个开源的、大规模、多种类别的数据集，由 TensorFlow 社区维护。它包含了各种用于机器学习和深度学习的数据集，包括 classification、multi-classification、object detection、image segmentation、text classification 等。

而 tensorflow_datasets.zip 则是 TensorFlow Datasets 中一个压缩包的名称，其中包含了 TensorFlow Datasets 中默认的一些数据集，包括 MNIST、CIFAR10、CIFAR100 等。这些数据集被经过封装和优化的接口封装，可以在 TensorFlow 中使用。用户只需简单调用相应的接口，即可轻松加载、使用这些数据集。

此外，用户还可以将自己的数据集加入到 tensorflow_datasets.zip 中，通过自定义的接口去访问和使用它。tensorflow_datasets.zip 的存在，大大降低了用户使用 TensorFlow Datasets 的难度和复杂度，使得对机器学习和深度学习感兴趣的开发者可以更加轻松地获取和使用数据集，从而更高效地进行实验和研究。

相关问题

module 'tensorflow.keras.datasets' has no attribute 'coco'

这个错误可能是因为 TensorFlow 版本更新后，`tensorflow.keras.datasets` 模块中已经没有 `coco` 数据集了。

你可以尝试使用其他数据集或者自己下载 COCO 数据集并手动加载。可以使用以下代码下载 COCO 数据集：

!wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip
!unzip annotations_trainval2017.zip -d annotations
!rm annotations_trainval2017.zip
!wget http://images.cocodataset.org/zips/train2017.zip
!unzip train2017.zip -d train2017
!rm train2017.zip
!wget http://images.cocodataset.org/zips/val2017.zip
!unzip val2017.zip -d val2017
!rm val2017.zip

然后你可以使用 `tfds.load()` 函数加载数据集：

import tensorflow_datasets as tfds

dataset, info = tfds.load('coco/2017', with_info=True)

记得要安装 TensorFlow Datasets 库，如果你还没有安装的话。

import tensorflow as tf from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPool2D, Dropoutfrom tensorflow.keras import Model​# 在GPU上运算时，因为cuDNN库本身也有自己的随机数生成器，所以即使tf设置了seed，也不会每次得到相同的结果tf.random.set_seed(100)​mnist = tf.keras.datasets.mnist(X_train, y_train), (X_test, y_test) = mnist.load_data()X_train, X_test = X_train/255.0, X_test/255.0​# 将特征数据集从(N,32,32)转变成(N,32,32,1),因为Conv2D需要(NHWC)四阶张量结构X_train = X_train[..., tf.newaxis] X_test = X_test[..., tf.newaxis]​batch_size = 64# 手动生成mini_batch数据集train_ds = tf.data.Dataset.from_tensor_slices((X_train, y_train)).shuffle(10000).batch(batch_size)test_ds = tf.data.Dataset.from_tensor_slices((X_test, y_test)).batch(batch_size)​class Deep_CNN_Model(Model): def __init__(self): super(Deep_CNN_Model, self).__init__() self.conv1 = Conv2D(32, 5, activation='relu') self.pool1 = MaxPool2D() self.conv2 = Conv2D(64, 5, activation='relu') self.pool2 = MaxPool2D() self.flatten = Flatten() self.d1 = Dense(128, activation='relu') self.dropout = Dropout(0.2) self.d2 = Dense(10, activation='softmax') def call(self, X): # 无需在此处增加training参数状态。只需要在调用Model.call时，传递training参数即可 X = self.conv1(X) X = self.pool1(X) X = self.conv2(X) X = self.pool2(X) X = self.flatten(X) X = self.d1(X) X = self.dropout(X) # 无需在此处设置training状态。只需要在调用Model.call时，传递training参数即可 return self.d2(X)​model = Deep_CNN_Model()loss_object = tf.keras.losses.SparseCategoricalCrossentropy()optimizer = tf.keras.optimizers.Adam()​train_loss = tf.keras.metrics.Mean(name='train_loss')train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')test_loss = tf.keras.metrics.Mean(name='test_loss')test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')​# TODO：定义单批次的训练和预测操作@tf.functiondef train_step(images, labels): ...... @tf.functiondef test_step(images, labels): ...... # TODO：执行完整的训练过程EPOCHS = 10for epoch in range(EPOCHS)补全代码

import tensorflow as tf
from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPool2D, Dropout
from tensorflow.keras import Model

# 在GPU上运算时，因为cuDNN库本身也有自己的随机数生成器，所以即使tf设置了seed，也不会每次得到相同的结果
tf.random.set_seed(100)

# 加载MNIST数据集并进行预处理
mnist = tf.keras.datasets.mnist
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train, X_test = X_train/255.0, X_test/255.0
X_train = X_train[..., tf.newaxis]
X_test = X_test[..., tf.newaxis]

# 定义batch_size并手动生成mini_batch数据集
batch_size = 64
train_ds = tf.data.Dataset.from_tensor_slices((X_train, y_train)).shuffle(10000).batch(batch_size)
test_ds = tf.data.Dataset.from_tensor_slices((X_test, y_test)).batch(batch_size)

# 定义深度CNN模型
class Deep_CNN_Model(Model):
def __init__(self):
super(Deep_CNN_Model, self).__init__()
self.conv1 = Conv2D(32, 5, activation='relu')
self.pool1 = MaxPool2D()
self.conv2 = Conv2D(64, 5, activation='relu')
self.pool2 = MaxPool2D()
self.flatten = Flatten()
self.d1 = Dense(128, activation='relu')
self.dropout = Dropout(0.2)
self.d2 = Dense(10, activation='softmax')

def call(self, X, training=False):
X = self.conv1(X)
X = self.pool1(X)
X = self.conv2(X)
X = self.pool2(X)
X = self.flatten(X)
X = self.d1(X)
X = self.dropout(X, training=training)
return self.d2(X)

# 实例化模型，定义损失函数和优化器
model = Deep_CNN_Model()
loss_object = tf.keras.losses.SparseCategoricalCrossentropy()
optimizer = tf.keras.optimizers.Adam()

# 定义训练和测试过程中的度量指标
train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')
test_loss = tf.keras.metrics.Mean(name='test_loss')
test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')

# 定义单批次的训练和预测操作
@tf.function
def train_step(images, labels):
with tf.GradientTape() as tape:
# 计算模型预测结果和损失函数值
predictions = model(images, training=True)
loss = loss_object(labels, predictions)
# 计算梯度并优化模型参数
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
# 更新训练过程中的度量指标
train_loss(loss)
train_accuracy(labels, predictions)

@tf.function
def test_step(images, labels):
# 计算模型预测结果和损失函数值
predictions = model(images, training=False)
t_loss = loss_object(labels, predictions)
# 更新测试过程中的度量指标
test_loss(t_loss)
test_accuracy(labels, predictions)

# 执行完整的训练过程
EPOCHS = 10
for epoch in range(EPOCHS):
# 在每轮训练开始时，重置度量指标的值
train_loss.reset_states()
train_accuracy.reset_states()
test_loss.reset_states()
test_accuracy.reset_states()

# 遍历训练数据集中的每个mini_batch进行训练
for images, labels in train_ds:
train_step(images, labels)

# 遍历测试数据集中的每个mini_batch进行预测
for test_images, test_labels in test_ds:
test_step(test_images, test_labels)

# 输出每轮训练和测试过程中的度量指标
template = 'Epoch {}, Loss: {}, Accuracy: {}, Test Loss: {}, Test Accuracy: {}'
print(template.format(epoch+1,
train_loss.result(),
train_accuracy.result()*100,
test_loss.result(),
test_accuracy.result()*100))